Some methods model the span detection with hypergraphs to account for the non-linear structure of the tag sequences. [1]} design a CRF hyper-graph with various node types to model entity types and boundaries. However, cycles in the graphs of some samples required that the CRF normalization term had to be approximated, leading to a decreased performance [2]}. [2]} models the mention edges and transitions instead of solely modeling token tags. Their method, however, requires multiple graphs if more there is more than one entity type. Alternatively, [4]} only models mention tags and not their transitions, but allows a multi-label prediction for each token. They modify an LSTM layer to represent multiple states for a single token and perform decoding during the neural network execution.
